What I Learned from Stanford XCS236: Deep Generative Models
I recently finished Stanford XCS236, and it was one of the most useful classes I have taken for building intuition around modern generative models. Before this course, I mostly understood model families as isolated islands: VAEs in one bucket, GANs in another, diffusion in a third. The class helped me see a single design space with explicit tradeoffs.
Why this class was valuable to me
The strongest part of XCS236 is that it does not stop at architecture diagrams. It keeps asking:
- What objective are we really optimizing?
- What assumptions are hidden in the model family?
- What failures should we expect from those assumptions?
That framing changed how I read papers and how I prototype models.
Core ideas that stuck
1) Generative modeling is mostly about tractable approximations
A lot of the course can be summarized as one practical reality: the true data distribution is inaccessible, so every model class is an approximation with a different computational budget.
- Autoregressive models trade slower sampling for exact likelihood.
- VAEs trade tighter likelihood for amortized inference.
- GANs optimize sample realism without explicit likelihood.
- Diffusion models trade many denoising steps for strong sample quality and stable training.
Instead of asking “which model is best,” I now ask “which tradeoff profile fits my deployment constraints?“
2) Latent variables are not just compression, they are structure
I used to think of latent variables mainly as dimensionality reduction. XCS236 made it clearer that latent spaces are where we inject inductive bias.
If the latent geometry is well aligned with semantic factors, interpolation, conditional generation, and downstream control all become easier. If not, everything looks good in aggregate metrics but feels brittle in real use.
3) Training objective and evaluation metric often disagree
Another practical lesson: optimizing ELBO, adversarial losses, or denoising objectives does not guarantee user-perceived quality. The course repeatedly emphasized combining:
- quantitative metrics (likelihood proxies, FID-like scores, reconstruction losses), and
- targeted qualitative checks (artifact patterns, mode collapse behavior, conditioning faithfulness).
This sounds obvious, but adopting it as a default workflow has saved me time.
4) Diffusion is conceptually simple but operationally nuanced
The forward-noise and reverse-denoise view is elegant, but implementation details matter a lot:
- noise schedules,
- parameterization choices,
- guidance strength,
- sampler choice versus latency target.
My biggest takeaway: diffusion quality comes from many small design decisions that interact, not one magic component.
A mental model I keep using
When I compare generative approaches now, I map them on three axes:
- likelihood tractability,
- sample quality,
- controllability/conditioning ease.
No family dominates all three. That is why model selection is usually an engineering decision, not a leaderboard decision.
Final reflection
XCS236 gave me a cleaner framework for reasoning about generative models under real-world constraints. The biggest shift was from “Which architecture is state of the art?” to “Which objective, inductive bias, and inference strategy match the product requirement?”
That question has made my research reading sharper and my implementation cycles shorter.